This series of files compile all analyses done during Chapter 1 for the local campaign (2014):
All analyses have been done with PRIMER-e 6 and R 3.6.0.
Click on the table of contents in the left margin to assess a specific analysis.
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Caracteristics of each campaign
| Sampling date |
|
August-September |
June to August |
July |
| Criteria for perturbation |
|
Potentially impacted if close to the city or industries, References outside the bay |
Human-impacted if in a region with a highly populated area, industries and maritime activities, Reference if none of these criteria |
Human-impacted if in a region with a highly populated area, industries and maritime activities, Reference if none of these criteria |
| Regions considered |
|
BSI |
BSI, CPC, BDA, MR |
BSI, MR |
| Number of sampled stations |
|
40 (20 HI, 20 R) |
78 (26 BSI, 19 CPC, 18 BDA, 15 MR) |
126 (111 BSI, 15 MR) |
| Parameters sampled |
Organic matter |
yes |
yes |
yes |
|
Photosynthetic pigments |
no |
yes |
yes |
|
Sediment grain-size |
yes |
yes |
yes |
|
Heavy-metals |
yes |
yes (for a limited number of stations) |
no (interpolated based on 2014 and 2016 values) |
| Benthic communities |
Compartment targeted |
Macro-infauna |
Macro-infauna |
Macro-infauna |
|
Sieved used |
500 µm |
1 mm |
500 µm and 1 mm |
|
Conservation technique |
Formaldehyle |
Formaldehyle |
Formaldehyle |
| Others |
|
N.A. |
N.A. |
N.A. |
We used data from subtidal ecosystems (see metadata files for more information). Only stations that have been sampled both for abiotic parameters and benthic species were included.
Selected variables for the analyses:
- Percentage of organic matter: om
- Percentage of gravel: gravel
- Percentage of sand: sand
- Percentage of silt: silt
- Percentage of clay: clay
- Concentration of arsenic: arsenic
- Concentration of cadmium: cadmium
- Concentration of chromium: chromium
- Concentration of copper: copper
- Concentration of iron: iron
- Concentration of manganese: manganese
- Concentration of mercury: mercury
- Concentration of lead: lead
- Concentration of zinc: zinc
- Species richness: S
- Abundance of total individuals: N
- Shannon index: H
- Piélou evenness: J
Abundances of Bipalponephtys neotena (Bneo) and Spisula solidissima (Ssol) were also considered (see IndVal and SIMPER results).
Statistics for each variable considered:
| depth |
4.000 |
9.600 |
7.250 |
6.970 |
1.611 |
0.255 |
0.499 |
| om |
0.187 |
8.260 |
0.868 |
1.368 |
1.465 |
0.232 |
0.454 |
| gravel |
0.000 |
0.481 |
0.000 |
0.017 |
0.076 |
0.012 |
0.024 |
| sand |
0.000 |
1.000 |
0.000 |
0.148 |
0.358 |
0.057 |
0.111 |
| silt |
0.000 |
0.022 |
0.001 |
0.004 |
0.006 |
0.001 |
0.002 |
| clay |
0.000 |
1.000 |
0.992 |
0.830 |
0.361 |
0.057 |
0.112 |
| arsenic |
1.100 |
6.000 |
2.250 |
2.720 |
1.259 |
0.199 |
0.390 |
| cadmium |
0.030 |
0.220 |
0.110 |
0.116 |
0.045 |
0.007 |
0.014 |
| chromium |
10.900 |
143.300 |
63.200 |
65.520 |
29.623 |
4.684 |
9.180 |
| copper |
2.200 |
32.400 |
7.300 |
11.045 |
8.675 |
1.372 |
2.688 |
| iron |
14089.920 |
188857.220 |
60284.230 |
64222.926 |
31677.444 |
5008.644 |
9816.761 |
| manganese |
251.670 |
5962.190 |
1106.625 |
1412.044 |
1050.987 |
166.176 |
325.698 |
| mercury |
0.000 |
0.250 |
0.000 |
0.014 |
0.043 |
0.007 |
0.013 |
| lead |
1.020 |
12.180 |
3.110 |
4.308 |
2.945 |
0.466 |
0.913 |
| zinc |
15.900 |
101.500 |
45.150 |
53.163 |
23.870 |
3.774 |
7.397 |
| S |
5.000 |
28.000 |
14.000 |
16.275 |
7.035 |
1.112 |
2.180 |
| N |
12.000 |
2100.000 |
173.500 |
634.000 |
702.288 |
111.041 |
217.637 |
| H |
0.776 |
2.364 |
1.800 |
1.694 |
0.386 |
0.061 |
0.120 |
| J |
0.294 |
0.935 |
0.636 |
0.640 |
0.152 |
0.024 |
0.047 |
1. Permutational Analyses of Variance
Results of univariate PermANOVAs on parameters and multivariate PermANOVA on the whole benthic community are presented in the table below.
| om |
S |
S |
(P1 P2 P3), (P4 R2), (R1 R2 R3) |
| gravel |
S |
|
(P1 P2 P3 P4 R3 R4), (R1 R2) |
| sand |
|
S |
All sites in the same group |
| silt |
S |
|
(P1 P2 P3 P4 R2 R3), (R1 R2), (R1 R4), (R2 R3 R4) |
| clay |
|
S |
(P1 P2 P3 P4), (P4 R1 R2 R3 R4), (R1 R2 R3), (R3 R4) |
| arsenic |
|
S |
(P1 P2), (P3 P4 R2), (P3 P4 R1 R3 R4) |
| cadmium |
|
S |
All except (P1 R2), (P1 R3), (P2 R2), (P2 R3), (P3 R2), (P3 R3) |
| chromium |
|
S |
(P1 P2 P3 R1 R4), (P4 R2 R3 R4) |
| copper |
S |
S |
(P1 P2 P3), (P1 P3 P4), (P4 R1 R2), (R1 R2 R3), (R2 R3 R4) |
| iron |
|
|
All except (P1 R3), (P2 R3), (R1 R3) |
| manganese |
|
S |
(P1 P2), (P3 P4 R1 R4), (R2 R3) |
| mercury |
|
|
(P1 P2 P3), (P2 P4 R1 R2 R3 R4) |
| lead |
|
S |
(P1 P2), (P1 P3), (P4 R1 R2 R3 R4) |
| zinc |
S |
|
(P1 P2 P3 P4), (P4 R1 R2 R4), (P4 R2 R3 R4) |
| S (500 µm) |
|
S |
(P1 P2 P3), (P4 R1 R3 R4), (P4 R2 R3 R4) |
| N (500 µm) |
|
S |
(P1 P2 P3), (P4 R2 R3 R4), (R1 R4) |
| H (500 µm) |
|
|
All except (P2 P3), (P3 P4) |
| J (500 µm) |
|
|
All except (P1 P4), (P1 R1), (P2 P3), (P2 P4), (P2 R1), (P2 R2) |
| ALL SPECIES (500 µm) |
S |
S |
(P1 P2), (R1 R4), (R2 R3) |
2. IndVal and SIMPER
These analyses allowed to select species as dependant variables for the regressions. We used results from PRIMER to justify further their choice.
## cluster indicator_value probability
## bipalponephtys_neotena 1 0.9490 0.001
## prionospio_steenstrupi 1 0.8969 0.001
## nephtys_sp 1 0.8494 0.001
## phyllodoce_groenlandica 1 0.8337 0.001
## phoronida 1 0.7986 0.001
## capitella_sp 1 0.7940 0.001
## scoloplos_armiger 1 0.7828 0.002
## cirratulidae_spp 1 0.7470 0.001
## limecola_balthica 1 0.7465 0.001
## sarsicytheridea_sp 1 0.6974 0.001
## eteone_sp 1 0.6386 0.001
## hediste_diversicolor 1 0.5500 0.001
## euchone_analis 1 0.4500 0.002
## pholoe_longa 1 0.4015 0.028
## pholoe_sp 1 0.3792 0.026
## pontoporeia_femorata 1 0.3500 0.015
## podocopida 1 0.3466 0.013
## diastylis_sculpta 1 0.3435 0.019
## glycera_dibranchiata 1 0.3360 0.008
## axinopsida_orbiculata 1 0.3000 0.016
## praxillella_praetermissa 1 0.3000 0.019
## sabellidae_spp 1 0.3000 0.018
## tharyx_sp 1 0.3000 0.024
## maldanidae_spp 1 0.2500 0.047
## spisula_solidissima 2 0.7515 0.002
## polygordius_sp 2 0.7397 0.001
## echinarachnius_parma 2 0.7000 0.001
## halacaridae_spp 2 0.2500 0.048
##
## Sum of probabilities = 67.676
##
## Sum of Indicator Values = 24.26
##
## Sum of Significant Indicator Values = 15.78
##
## Number of Significant Indicators = 28
##
## Significant Indicator Distribution
##
## 1 2
## 24 4
SIMPER results (average dissimilarity: 96.41 )
| bipalponephtys_neotena |
0.272 |
0.152 |
1.79 |
425 |
0.45 |
0.282 |
| nephtys_sp |
0.222 |
0.143 |
1.55 |
345 |
0.25 |
0.513 |
| prionospio_steenstrupi |
0.0581 |
0.065 |
0.895 |
58 |
0.2 |
0.573 |
| scoloplos_armiger |
0.0439 |
0.0524 |
0.838 |
63.5 |
1.4 |
0.618 |
| spisula_solidissima |
0.0398 |
0.0919 |
0.433 |
1.25 |
19.4 |
0.66 |
| phoronida |
0.0345 |
0.0372 |
0.926 |
56.9 |
0.1 |
0.695 |
| phoxocephalus_holbolli |
0.0249 |
0.0535 |
0.465 |
4.65 |
16.2 |
0.721 |
| polygordius_sp |
0.024 |
0.0971 |
0.247 |
0.5 |
36 |
0.746 |
| phyllodoce_groenlandica |
0.0207 |
0.0195 |
1.06 |
25.5 |
0.5 |
0.768 |
| harpacticoida |
0.0207 |
0.0446 |
0.463 |
10.9 |
10.2 |
0.789 |
| capitella_sp |
0.0198 |
0.0217 |
0.911 |
26.2 |
0.2 |
0.81 |
| mytilus_sp |
0.0153 |
0.0636 |
0.241 |
0.3 |
15.9 |
0.826 |
| oligochaeta |
0.014 |
0.0528 |
0.265 |
1.5 |
4.45 |
0.84 |
| echinarachnius_parma |
0.0136 |
0.0382 |
0.356 |
0 |
6.8 |
0.854 |
| limecola_balthica |
0.0109 |
0.0175 |
0.621 |
10.6 |
0.05 |
0.865 |
| hediste_diversicolor |
0.0105 |
0.0416 |
0.252 |
2.95 |
0 |
0.876 |
| pholoe_minuta_tecta |
0.0099 |
0.0374 |
0.265 |
4.95 |
2.75 |
0.887 |
| glycera_sp |
0.00984 |
0.0295 |
0.333 |
1.35 |
0 |
0.897 |
3. Univariate regressions
Independant variables are habitat parameters and heavy metal concentrations, dependant variables are diversity indices. We used linear models for the all regressions on diversity indices.
3.1. Identification of outliers
To identify stations that are not consistent with the others, we used the multivariate Cook’s Distance (CD) on the uncorrelated variables. A significative threshold of 4 times the mean of CD has been established.

Based on Cook’s Distance, we identified stations 1, 19 and 29 as general outliers. They have been deleted for the following analyses.
3.2. Correlations between parameters
Correlations have been calculated with Spearman’s rank coefficient.
Correlation coefficients between habitat parameters and metals concentrations
| om |
1 |
-0.606 |
-0.137 |
-0.439 |
0.649 |
0.575 |
0.288 |
0.175 |
0.785 |
-0.066 |
0.372 |
0.702 |
0.641 |
0.661 |
| gravel |
-0.606 |
1 |
0.236 |
0.332 |
-0.754 |
-0.419 |
-0.255 |
-0.162 |
-0.524 |
-0.013 |
-0.384 |
-0.536 |
-0.569 |
-0.607 |
| sand |
-0.137 |
0.236 |
1 |
-0.644 |
-0.67 |
-0.327 |
-0.512 |
-0.579 |
-0.415 |
-0.545 |
-0.507 |
-0.297 |
-0.456 |
-0.504 |
| silt |
-0.439 |
0.332 |
-0.644 |
1 |
-0.086 |
-0.143 |
0.227 |
0.345 |
-0.164 |
0.418 |
0.072 |
-0.233 |
-0.128 |
-0.099 |
| clay |
0.649 |
-0.754 |
-0.67 |
-0.086 |
1 |
0.602 |
0.522 |
0.476 |
0.707 |
0.312 |
0.624 |
0.67 |
0.782 |
0.809 |
| arsenic |
0.575 |
-0.419 |
-0.327 |
-0.143 |
0.602 |
1 |
0.482 |
0.416 |
0.681 |
0.291 |
0.572 |
0.584 |
0.68 |
0.612 |
| cadmium |
0.288 |
-0.255 |
-0.512 |
0.227 |
0.522 |
0.482 |
1 |
0.855 |
0.519 |
0.725 |
0.822 |
0.452 |
0.792 |
0.775 |
| chromium |
0.175 |
-0.162 |
-0.579 |
0.345 |
0.476 |
0.416 |
0.855 |
1 |
0.448 |
0.888 |
0.82 |
0.445 |
0.744 |
0.719 |
| copper |
0.785 |
-0.524 |
-0.415 |
-0.164 |
0.707 |
0.681 |
0.519 |
0.448 |
1 |
0.286 |
0.587 |
0.646 |
0.729 |
0.837 |
| iron |
-0.066 |
-0.013 |
-0.545 |
0.418 |
0.312 |
0.291 |
0.725 |
0.888 |
0.286 |
1 |
0.708 |
0.174 |
0.58 |
0.566 |
| manganese |
0.372 |
-0.384 |
-0.507 |
0.072 |
0.624 |
0.572 |
0.822 |
0.82 |
0.587 |
0.708 |
1 |
0.591 |
0.832 |
0.792 |
| mercury |
0.702 |
-0.536 |
-0.297 |
-0.233 |
0.67 |
0.584 |
0.452 |
0.445 |
0.646 |
0.174 |
0.591 |
1 |
0.728 |
0.659 |
| lead |
0.641 |
-0.569 |
-0.456 |
-0.128 |
0.782 |
0.68 |
0.792 |
0.744 |
0.729 |
0.58 |
0.832 |
0.728 |
1 |
0.914 |
| zinc |
0.661 |
-0.607 |
-0.504 |
-0.099 |
0.809 |
0.612 |
0.775 |
0.719 |
0.837 |
0.566 |
0.792 |
0.659 |
0.914 |
1 |

According to these results, the following variables are highly correlated (\(|\rho|\) > 0.80) so they have been considered together in the regressions:
- cadmium, chromium and manganese concentrations (cadmium and manganese deleted)
- lead and zinc concentrations (zinc deleted)
We also decided to exclude clay content in the regressions, as it tends to increase drasticaly VIFs due to a marginal negative correlation with sand (very high \(R^{2}\)).

3.3. Simple regressions
These analyses have been done to explore the relationships between variables. As it is a huge number of results to interpret, only multiple regressions will be included in the article.
Adjusted R-squared of simple regressions with all variables
| S |
0.3729 |
0.1572 |
0.005039 |
0.2358 |
0.3219 |
0.002971 |
0.4527 |
-0.02687 |
0.3132 |
0.4404 |
| N |
0.4488 |
0.1816 |
0.04396 |
0.2275 |
0.56 |
0.1497 |
0.6473 |
-0.01284 |
0.2645 |
0.7273 |
| H |
0.01303 |
-0.0257 |
-0.009711 |
-0.02158 |
-0.02834 |
-0.005307 |
0.01352 |
0.007732 |
0.0008677 |
-0.02508 |
| J |
0.03825 |
0.02916 |
-0.02281 |
0.07349 |
0.1648 |
0.01825 |
0.06347 |
-0.01873 |
0.01996 |
0.147 |
p-values of simple regressions with all variables
| S |
3.576e-05 |
0.008752 |
0.2843 |
0.001363 |
0.0001491 |
0.2999 |
3.047e-06 |
0.8111 |
0.0001886 |
4.546e-06 |
| N |
3.466e-06 |
0.004978 |
0.1122 |
0.001671 |
6.116e-08 |
0.01038 |
1.193e-09 |
0.4658 |
0.0006679 |
1.263e-11 |
| H |
0.2326 |
0.7561 |
0.4242 |
0.6277 |
0.9301 |
0.3743 |
0.2299 |
0.2655 |
0.3168 |
0.7321 |
| J |
0.1279 |
0.158 |
0.6597 |
0.05756 |
0.007336 |
0.2048 |
0.07208 |
0.5646 |
0.1965 |
0.01103 |
3.4. Multiple regressions
This section presents analyses done (i) to determine which model (metals, parameters or all) describes the best the parameters and (ii) which variables are the most important to explain the parameters.
3.4.1. Best model selection
The aim here is to know which model is the best to explain our data.
Species richness
| Full model |
37 |
12 |
231.6 |
6.506 |
0.56 |
| Parameters |
37 |
6 |
234.5 |
9.361 |
0.46 |
| Metals |
37 |
8 |
225.1 |
0 |
0.6 |
Total abundance
| Full model |
37 |
12 |
539.8 |
0.8106 |
0.81 |
| Parameters |
37 |
6 |
566.2 |
27.15 |
0.56 |
| Metals |
37 |
8 |
539 |
0 |
0.8 |
Shannon index
| Full model |
37 |
12 |
48.1 |
5.671 |
-0.08 |
| Parameters |
37 |
6 |
43.2 |
0.7641 |
-0.06 |
| Metals |
37 |
8 |
42.43 |
0 |
0.01 |
Piélou’s evenness
| Full model |
37 |
12 |
-25.59 |
4.892 |
0.02 |
| Parameters |
37 |
6 |
-30 |
0.4854 |
0.02 |
| Metals |
37 |
8 |
-30.49 |
0 |
0.08 |
3.4.2. Significative variables selection
We identified which variables were selected after an AIC procedure to predict the best the parameters. Results of the variable selection, according to AIC, are shown on the tables below:
- for the model with all variables
| om |
|
+ |
|
|
| gravel |
|
|
|
|
| sand/clay |
|
|
|
- |
| silt |
|
|
|
|
| arsenic |
|
|
|
- |
| chromium/cadmium/manganese |
|
|
- |
|
| copper |
|
|
+ |
|
| iron |
- |
- |
|
|
| mercury |
+ |
|
|
|
| lead/zinc |
+ |
+ |
|
|
| Adjusted \(R^{2}\) |
0.62 |
0.83 |
0.07 |
0.19 |
- for the model with habitat parameters
| om |
+ |
+ |
|
|
| gravel |
|
|
|
|
| sand/clay |
- |
- |
|
|
| silt |
- |
- |
|
+ |
| Adjusted \(R^{2}\) |
0.47 |
0.57 |
0 |
0.07 |
- for the model with heavy metals
| arsenic |
|
|
|
|
| chromium/cadmium/manganese |
- |
|
- |
|
| copper |
|
|
+ |
|
| iron |
|
- |
|
|
| mercury |
+ |
+ |
|
|
| lead/zinc |
+ |
+ |
|
- |
| Adjusted \(R^{2}\) |
0.63 |
0.81 |
0.07 |
0.15 |
Details of the regressions, with diagnostics and cross-validation, are summarized below.
Parameters
Shannon index
## FULL MODEL
## Adjusted R2 is: -0.06
Fitting linear model: H ~ om + gravel + sand + silt
| (Intercept) |
1.672 |
0.1527 |
10.95 |
2.37e-12 |
* * * |
| om |
0.03866 |
0.05214 |
0.7414 |
0.4638 |
|
| gravel |
3.286 |
8.187 |
0.4013 |
0.6909 |
|
| sand |
-0.1739 |
0.2271 |
-0.7657 |
0.4495 |
|
| silt |
-7.438 |
15.63 |
-0.476 |
0.6373 |
|
Variance Inflation Factors
| VIF |
1.18 |
1.23 |
1.27 |
1.42 |
## REDUCED MODEL
## Adjusted R2 is: 0
Fitting linear model: H ~ 1
| (Intercept) |
1.681 |
0.06334 |
26.54 |
3.127e-25 |
* * * |
Quitting from lines 413-417 (C1_analyses_loc2.Rmd) Error in Qr$qr[p1, p1, drop = FALSE] : indice hors limites De plus : There were 26 warnings (use warnings() to see them)
## RMSE for the full model: 0.4727543
## RMSE for the reduced model: 0.3973865



4. Multivariate regression
Independant variables are habitat parameters and heavy metal concentrations, dependant variables are species abundances. Outliers and correlated variables have been excluded from the analysis.
This analysis has been done on PRIMER, with a DistLM to identify the variables that explain the most the community variability and with a dbRDA to plot the results.
